\title{CS531 Programming Assignment 4: Wumpus Agent}
\author{
        Michael Lam, Xu Hu \\
        EECS, Oregon State University\\
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}
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\begin{abstract}
In this assignment we design, implement and evaluate an algorithm that uses first-order logic and A* search for an agent in order to solve Wumpus puzzles.
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\section{Introduction}

The Wumpus world is a 4x4 grid containing pits, one Wumpus and one gold at various locations. The objective of the agent is to retrieve the gold without dying from the Wumpus or falling into a pit. Furthermore the agent can perceive its environment and infer the location of pits and the Wumpus due to the rules of constructing a Wumpus world. Therefore it makes sense to implement an algorithm involving logic and search to make intelligent decisions.

We used the existing Wumpus environment simulator for Python provided by Walker Orr. We designed an agent that uses first-order logic with a tell-ask interface to assert/query what it knows about the Wumpus world and A* search to plan routes around the Wumpus world. The algorithm is essentially the same as the one provided in the Russell-Norvig textbook (pg. 270, fig. 7.20). For answering logic queries, the program uses the Prover-9 program.

To evaluate our algorithm, we collected statistics on the agent such as if the agent were successful. We ran experiments on 40 randomly generated maps (though some are unsolvable with gold in the same square as a pit). We also evaluated our agent using an RBFS algorithm instead of A* and compared results.

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